The present invention relates generally to air data sensing systems, such as flush air data systems (FADS), for use on an air vehicle. More particularly, the present invention relates to methods and apparatus for providing fault isolation in artificial intelligence based air data sensing systems, such as neural network based FADS.
A FADS typically utilizes several flush or semi-flush static pressure ports on the exterior of an air vehicle (such as an aircraft) to measure local static pressures at various positions. The pressure or pressure values measured by the individual ports are combined using some form of artificial intelligence algorithm(s), e.g., neural networks (NNs) for instance, to provide corrected air data parameters for the air vehicle. Corrected air data parameters represent global values of these parameters for the air vehicle. In this context, the term “global” refers to the air data measured far away from the air vehicle, i.e., “far field.” In contrast, “local” parameters are measured at the surface of the air vehicle and are prone to flow field effects around the aircraft geometry. Local parameters are characterized, or corrected, in order to get global air data. Examples of these global air data parameters for the air vehicle include angle of attack (AOA), angle of sideslip (AOS), Mach number, etc. Other well known global air data parameters for the air vehicle can also be calculated. Another example of artificial intelligence algorithms which can be used with a FADS is support vector machines (SVMs), and artificial intelligence algorithms as referenced herein include these or other types of algorithms which learn by example.
Flush air data systems provide numerous advantages which make their use desirable for certain air vehicles or in certain environments. For example, the flush or semi-flush static pressure ports can result in less drag on the air vehicle than some other types of pressure sensing devices. Additionally, the flush or semi-flush static pressure sensing ports experience less ice build-up than some other types of pressure sensing devices. Other advantages of a FADS can include, for example, lower observability than some probe-style air data systems.
Consider a FADS which uses N flush static pressure ports for use on an aircraft. The individual ports each measure a single local pressure value related to their respective locations on the aircraft. Using neural networks or other artificial intelligence algorithms, these N pressure values can be used as inputs to provide the individual global air data parameters necessary for the air data system. To ensure accurate performance and to increase reliability, an important part of the overall air data system is the ability to isolate and detect faults to maintain accuracy and safety levels. Blocked ports or drifting sensors are examples of failures of hardware. Drifting sensors are sensors with an output which changes over time, due to calibration or other problems, relative to a desired or baseline output for a particular set of conditions. Undetected faults reduce the safety of the overall system, and since aircraft global parameters are derived using artificial intelligence with a large number of pressure sensing ports as inputs, failure of one or more of these ports can be difficult to identify and isolate. Therefore, there is a need for methods of fault isolation in artificial intelligence based FADS or other air data systems.